Inclusive governance and sustainable value co-creation in circular business models: The case of ‘neighbourhood hubs against food waste’ in Milan

• Collaboration is hindered by conflicting paradigms, crisis-driven service delivery, and lack of shared decision-making, compounded by systemic issues like high workloads and underreported DFV. • Co-located, cross-sector roles enhance collaboration between DFV services and child protection agencies by improving communication, transparency, and bridging knowledge gaps. • Developing a shared language for risk and safety assessments aligns DFV and child protection perspectives, enabling structured case discussions that reduce victim-blaming and empower non-offending parents • Strengthening collaboration requires policies mandating joint decision-making, cross-training, adequate funding, and trauma-informed approaches that integrate DFV expertise into child protection. Collaboration between statutory child protection and domestic family violence agencies has been identified as best practice to ensure that men who use violence are visible, and women and children are safe when the violence results in child protection concerns. This paper articulates enablers and barriers that emerged from a research project that tested a co-designed collaborative service model at the intersection of child protection and domestic and family violence in [jurisdiction]. The research was conducted between August 2019 and September 2021. Examination of the enablers and barriers were identified using multiple data sources including case discussions, case file reviews, and working group deliberations. Analysis of the data identified that the Specialist Liaison Officer (SLO) was instrumental in enhancing communication, integrating the key elements of the collaborative service model, and nurturing of professional alliances. However, differences of underlying conceptual and ideological paradigms, service mandates and unilateral decision-making processes driven by legislation significantly impeded effective collaborative service approaches. Domestic and Family Violence (DFV) is a significant issue for most families who are involved with child protection services and therefore building organisational capacity, shared understanding of practice and professional alliances across these complex service sectors is deserving of investment and further research.

Integrating Evidence-Based Early Relational Health Programs Into Pediatric Primary Care: A Mixed Methods Study.

Collaborative model between breast surgery and genetic counseling reduces wait times and increases pretesting genetic counseling volume.

Assistive robots can support collaborative manipulation tasks such as carrying heavy or extended objects. As the human–human interaction is the basis for human–robot interaction, it is important to understand and quantify primarily haptic interaction. The subjects’ movements were recorded with a 3D motion capture system to determine spatio–temporal and upper and lower body kinematic parameters. The human–human interaction provided foundational data on human movement in collaborative manipulation tasks. The task with the robot revealed almost no changes in upper body kinematics, however, it was slower and showed adaptations of the human movement in the center of mass motion and in spatio–temporal parameters and lower body kinematics. This shows, that analyzing the interaction between humans and assistive robots focusing on human movement is essential for further developing assistive robots. • Humanhuman carrying dynamics serve as a model for humanrobot collaboration. • 3D motion capture analysis of human and robot-assisted table-carrying tasks. • Human–robot-interaction during joint carrying evoke adaptations in human motion. • Upper body kinematics remain similar in humanrobot and humanhuman interaction. • Findings inform robotic design for natural humanrobot collaborative manipulation.

How digital intelligence technologies reshape emergency collaboration: An agent-based model of multi-actor governance

Competencies needed for pharmacist prescribing: A qualitative study of competency frameworks to identify gaps in Dutch pharmacy education.

Green building research is shifting from a sole focus on physical performance to a human-centered, collaborative approach that integrates environmental sustainability with user well-being. However, a critical gap remains in understanding how built environments influence physiological, emotional, and cognitive processes. This review examines the integration of neuroscientific tools - including electroencephalography (EEG), functional magnetic resonance imaging (fMRI), event-related potentials (ERPs), eye-tracking (ET), and functional near-infrared spectroscopy (fNIRS) - into green building research. These technologies enable objective and fine-grained measurement of human responses to architectural spaces. We demonstrate how multimodal neurotechnologies facilitate real-time detection of human–environment interactions, supporting dynamic spatial optimization, health-oriented performance enhancement, and the subconscious reinforcement of sustainable behaviors. Beyond synthesizing empirical evidence, we propose an AI-augmented collaborative design framework that connects neural data with environmental parameters, bridging aesthetic, scientific, technical, and ethical rationalities. This framework provides a transformative pathway towards carbon neutrality while enhancing cognitive and emotional well-being, positioning neuroscience as a cornerstone of next generation green building research. • Integrates neuroscience (EEG, fMRI, ET, fNIRS) into green building to objectively measure user responses. • Proposes a collaborative design model bridging aesthetics, science, tech, and ethics for synergy. • Uses multimodal neural-environmental-behavioral data fusion for precise human-building interaction. • Shifts green building paradigm from human adaptation towards neuro-adaptive environments empowering users. • Uses neuro-responsive design to subconsciously drive sustainable behaviors and carbon goals.

Multi-type electricity market collaborative trading optimization model for stimulating the regulation potential of flexible resources: Experience from China's pumped storage plants

Best-Practice Mental Health Interventions Following Acute Cardiovascular Events.

Two-phase collaborative model compression training for joint pruning and quantization.

In mass customization, order tasks must strike a balance between batch production and customization, minimizing costs while ensuring fast delivery. Effective collaboration between enterprises and suppliers necessitates careful consideration of order allocation granularity and dynamic resource production capacities to achieve optimal task distribution. This paper introduces a multi-order collaborative allocation model based on a product-process mix, aimed to address the order allocation challenges faced by core manufacturing enterprises in cloud manufacturing under mass customization. First, the limitations of existing order decomposition and resource constraint strategies in the order allocation process are analyzed. A product-process mixed multi-order collaborative allocation model is then established, focusing on minimizing multiple-order costs while accounting for dynamic resource capacity constraints. Next, an improved ant colony algorithm (IACO-a&b) is proposed, tailored to the features of the model. This algorithm enhances the initialization of the ant search solution space by combining the optimal solution searched by the ants, while dynamically adjusts pheromone volatility coefficients and sets path pheromone concentration intervals, so as to avoid preventing the algorithm from precociousness. Finally, the performance of IACO-a&b is compared with several other algorithms, and the proposed order allocation model is evaluated against traditional models in this paper. The experimental results demonstrate that, in large-scale case studies, IACO-a&b improves the best fitness value by 11.89% and increases the fitness excellence rate by 60%. Furthermore, the proposed model reduces the leveling volatility index (LVI) of resources by 50.79% without significantly increasing order costs.

Real-world data (RWD) and real-world evidence (RWE) are increasingly important in oncology, where randomized controlled trials often exclude elderly patients, those with comorbidities, and rare cancer subtypes. To meet the need for high-quality oncology RWD in Japan, the J-CONNECT Consortium was launched to build a multiinstitutional database of chemotherapy-treated solid cancer patients using electronic medical records (EMRs). Data were collected from 12 Ministry of Health, Labour and Welfare-designated cancer care hospitals, with expansion to 15 institutions planned by 2026. J-CONNECT links hospital cancer registry information with EMRs, capturing demographics, staging, treatments, laboratory results, and biomarkers. Coverage and distributional characteristics were evaluated by comparing distributions with the National Hospital-based Cancer Registry by cancer site, age, and sex. The database included 51,497 patients diagnosed after 2018. Major cancer groups were breast (12,363), lung (6743), prostate (5235), colorectal (4012), pancreas (3720), stomach (2383), esophagus (1960), and liver (1731). Sex distributions were consistent with national data, while some variability was observed by age. J-CONNECT is a robust oncology database in which clinical and biomarker variables are available, although completeness may vary; fitness-for-purpose should be assessed on a study-by-study basis. Coverage and distributional characteristics and also regulatory acceptance highlight its value for research, PMS, and policy. Future directions include expansion to 25 hospitals, integration of outcome and resource-use data, and adoption of common data models for broader collaboration. We believe J-CONNECT provides valuable oncology data supporting research, post-marketing surveillance, and public health policy in Japan.

Recent technological development has made multirobot collaboration a promising approach for tasks in unstructured and dynamically changeable environments. However, challenges still occur especially in reliability, efficiency and system integration. This review systematically examines former research and experiments, with focus on robust perception, collaborative planning and hybrid architectures. It poses that there requires advancement in communication-control co-design, lightweight collaboration models, and standardized tests. The study concludes that future progress should depend on adaptable and cost-effective systems capable of establishing positive human-robot relationship, which requires combination of robotics, AI and edge computing.

The population of children and youth with special health care needs (CYSHCN) has grown significantly. This includes children with medical complexity (CMC), those with chronic conditions, functional limitations, or reliance on medical technology. Due to advances in medical care, these children are now able to live at home, making encounters with emergency medical services (EMS) more likely. EMS clinicians receive limited pediatric training and have infrequent encounters with this population, resulting in variable comfort and preparedness. Utilization of emergency care among CMC is further influenced by social determinants of health, with disadvantaged communities experiencing higher emergency department use and increased child mortality. To bridge these gaps, the Special Needs Tracking and Awareness Response System (STARS) was developed to enhance EMS readiness, promote health equity, and improve prehospital care for CMC. Launched in 2014 as an EMS-driven initiative, STARS has evolved into a hospital-based, physician-led program with individualized emergency care plans stored in a secure electronic system. A major focus of STARS is to create and provide emergency care education to EMS and community EDs in their catchment area regarding STARS. As of 2025, STARS has enrolled 2424 patients. The program has reduced unnecessary transports, strengthened disaster response, and offered an opportunity to address health inequities in CMC. STARS provides a scalable and collaborative model that prioritizes medically complex, high-risk pediatric populations through targeted EMS training, interdisciplinary care coordination, and real-time access to patient-specific plans. This approach offers a unique opportunity to advance prehospital care and improve health outcomes for CMC.

Purpose This paper aims to examine how manufacturing enterprises manage the organizational changes triggered by artificial intelligence (AI) adoption and theorizes the staged evolution of human–AI collaboration through shifts in routines and dominant logic. Design/methodology/approach Drawing on dominant logic and routine dynamics theory, we apply the Gioia methodology to analyze in-depth interview data from 30 manufacturing managers. Findings The analysis uncovers a three-stage evolution of dominant logics – shifting from human-centric to alignment optimization and ultimately to value co-creation. Each stage drives changes in organizational routine, deepening the collaboration between humans and AI. Originality/value This paper proposes a theoretical model that captures the progression of human–AI collaboration. This model advances our understanding of AI-driven organizational change and provides actionable insights for manufacturers seeking intelligent transformation.

As young children increasingly interact with information and communication technologies, they face significant digital safety risks, including exposure to explicit content, online grooming, and the creation of self-generated child sexual abuse material. Despite these growing threats, South Africa lacks a structured digital safety framework within its national early childhood education curriculum, despite the possibility of integration within existing subjects. This study aims to develop a model for effective digital safety education in early childhood. Guided by the digital phallic development framework, which integrates Freud’s psychosexual development theory (specifically the phallic stage) with the CO:RE 3 C’s model of digital safety risks, this study offers a comprehensive lens for understanding sexual digital safety risks in young children. It is further underpinned by the digital safety education collaboration (DSEC) model, highlighting the essential intersectoral partnerships needed for effective digital safety education. Employing a qualitative, constructivist methodology, data were gathered through semi-structured interviews with six stakeholders representing the sectors identified in the DSEC model. Additionally, four authentic South African policy and guidance documents referencing digital safety were analysed. Thematic analysis, supported by Atlas.ti and ChatGPT, revealed a critical need for developmentally appropriate, holistic digital safety education and identified gaps in policy, training, and intersectoral communication. The findings underscore the urgency of coordinated action among the education, community, and legal sectors to empower and protect young children from sexual digital risks in an increasingly digital world. This study contributes to scholarship by creating a theoretical and practical framework for digital safety education in early childhood.

Collaborative care model for perinatal wellness support services - Population-level equity-centered systems change (COMPASS+) protocol paper.

Considerable time spent on delivering the collaborative care model (CoCM) for patients with co-occurring opioid use and mental health conditions is not billable under relevant billing codes for Medicare or Medicaid behavioral health integration. In this follow-up study, the authors explored associations between patients' characteristics and the likelihood of receiving unbillable services. Billable time was estimated by applying billing code rules to documentation of CoCM delivery from a clinical trial (N=381 patients). Multivariate logistic regressions revealed patient characteristics associated with receiving >15 minutes in ≥1 months of unbillable outreach before CoCM engagement or care time that exceeded code maxima. Overall, 43% of the patients received unbillable outreach, and 59% exceeded billable time. Health system, age, stimulant use, and worry about housing were significantly or marginally associated with exceeding billable time; health system also predicted unbillable outreach. Policymakers should consider modifying CoCM billing codes to adequately reimburse efforts to engage and serve this complex patient population.

This study aimed to explore integrative oncology (IO) across the Middle East, examine how international IO leaders promote IO in the Middle East, and assess how the Middle East contributes to the development of IO globally. This narrative review employed a structured literature search and descriptive analytic approach, using multiple databases, predefined keywords, varied study designs, and multilingual sources. It was conducted by a multi-disciplinary team of clinicians, researchers, and medical educators from ten Middle Eastern countries (Cyprus, Egypt, Iran, Israel, Jordan, Morocco, Palestine, Sudan, Turkey, and United Arab Emirates) and global IO/palliative leaders from South America, India, China, Europe, and the USA, providing a real-life implementation perspective. The collaborative IO model of care ensures safe and effective traditional and herbal medicine practices, with a culturally-sensitive approach respecting health beliefs. Middle Eastern and international IO participants discuss how they can benefit from each other, drawing from Middle-East Research Group in Integrative Oncology (MERGIO) activities in clinical practice, research, and medical education. Important themes derived from the review include the potential for IO to advance patient-centered care, effectively improve quality of life-related concerns, ensure a safe therapeutic environment, and overcome accessibility- and disparity-related barriers. IO treatments should be provided by trained multi-disciplinary teams of integrative physicians and practitioners, within culturally-adapted multi-modal programs and a receptive national and regional healthcare policy. Research utilizing pragmatic methodologies reflects real-world IO practice, with IO prioritized in medical education providing students and healthcare professionals with high-quality clinical and research training across the Middle East.